Assignment Sample on U20859/U25188 M452 Operations Management
Problem 1: Discussion on given question
- i) Effective and efficient:
Both the terms provide different meanings within operations of a manufacturing company. Efficient indicates achieving maximum productivity at a minimum effort and wastage of resources. Effective refers to attaining success to produce a desired result. Observing efficiency within manufacturing production is possible by looking at good quality productive work against earned hours (Patnaik et al. 2021). Effective manufacturing signifies an older version of an objective is being replaced by a newer version during production. For example, an individual prepares personalized emails and targets 20 potential clients to send those emails. In return for this effort, the individual gets 30% of emails that lead to sales. Therefore, this is a case where both effectiveness and efficiency becomes visible.
- ii) Effective and not efficient:
A manufacturing unit is effective when it produces intended results at the same time it becomes efficient by operating with least resources. Therefore, it is possible for that manufacturing unit to be effective but without being effective. For example, 100 generic sales emails are sent by an individual to potential clients out of which 2% of emails lead to sales (Braglia et al. 2018). On the other hand, another individual sent 10 tailored emails to potential clients and out of which 40% of emails lead to sales. Therefore, this example shows the concept of being effective but not efficient enough to generate sales.
iii) Not effective and efficient: The vice-versa scenario might come up where an individual being a part of a manufacturing unit is not effective but highly efficient. In such a scenario, an organization is unable to compete within a competitive environment and eventually move towards bankruptcy.
- iv) Not effective and not efficient: An ineffective and inefficient manufacturing unit is going to be obsolete by being unable to provide fewer sales (Meister et al. 2019). For example, a unit is able to satisfy demand of only 20 potential clients. This leads to 1% of sales which is quite low to remain profitable within a competitive environment.
Problem 2: Aggregate planning for SC
Discussion based on mathematical models
Scenario A
- Optimum production schedule is a framework that is being developed by a company within a production plan for encompassing sub-optimal levels of production and remanufacturing of a specific product. As opined by Liu et al. (2021), scheduling an optimal production plan becomes visible within a manufacturing system by associating with recovery process. In this scenario, information related to Acorn Manufacturing unit has been used to prepare the schedule.
| Particulars | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
| Regular Production | 1100 | 1230 | 1210 | 1326 | 1551 | 1310 | 1100 | 1100 | 1200 | 1200 | 1510 | 1300 |
| Regular Production cost | 160000 | 160000 | 160000 | 160000 | 160000 | 160000 | 160000 | 160000 | 160000 | 160000 | 160000 | 160000 |
| Overtime Production Cost | 400000 | 400000 | 400000 | 400000 | 400000 | 400000 | 400000 | 400000 | 400000 | 400000 | 400000 | 400000 |
| Units Produced | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 |
| Sales commitment | 600 | 600 | 600 | 600 | 600 | 600 | 600 | 600 | 600 | 600 | 600 | 600 |
| Inventory | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 | 5000 |
| Actual Sales | 490 | 500 | 510 | 520 | 530 | 540 | 550 | 560 | 570 | 580 | 590 | 600 |
| Shortage | 110 | 100 | 90 | 80 | 70 | 60 | 50 | 40 | 30 | 20 | 10 | 0 |
| Shortage cost | 100000 | 100000 | 100000 | 100000 | 100000 | 100000 | 100000 | 100000 | 100000 | 100000 | 100000 | 100000 |
| Inventory carrying cost | 150000 | 150000 | 150000 | 150000 | 150000 | 150000 | 150000 | 150000 | 150000 | 150000 | 150000 | 150000 |
| Total monthly cost | 818300 | 818430 | 818410 | 818526 | 818751 | 818510 | 818300 | 818300 | 818400 | 818400 | 818710 | 818500 |
| Annual Production Cost | 9821537 |
Table 1: Monthly Production Cost
(Source: Ms-Excel)
The above calculated values project optimum production schedule within a year of Acorn manufacturing unit. It has been formulated with an ideology to maintain a balance between delivery performance and efficiency. Therefore, this balance is going to bring increased revenue by minimizing bottlenecks within production (Basiri, Z. and Pourrahimian, 2018). This company wants its inventory value to be zero at the end of the year. As a result, chalking out the optimized schedule is going to synchronize supply with existing demand for reducing inventories.
- ii. Overtime:
The management of Acorn wants to increase over time from 20 hours to 40 hours which indicates the employees have to work double. Doubling the OT is not acceptable and negotiable as minimum increase sits around 3% (Xu et al. 2018). Therefore, management has no remaining value to increase the OT.
Scenario B
Optimal production:
Optimal production refers to an output organization is able to achieve through maximizing production as well as profitability level. Achieving optimal production provides output when marginal revenue of a particular saleable unit is equal to the marginal cost (Malik and Kim, 2020). The value of optimal production is being determined to build up a relationship between revenue gained after sale of a product and production cost of a future product.
Hiring:
The cost related to hiring new employees is being done through a metric known as hiring cost. Acorn Manufacturing unit has to bear £800 for hiring 50 new employees and this amount is being included while computing optimal production cost.
Layoff Schedule:
The act of terminating or suspending employees from ongoing operations is known as laying off. Preparing a schedule for estimating layoff is going to extract the amount of income an organization generated from operations (Tao et al. 2021). In this scenario, Acorn is going to have £1200 per employee.
| New employees | 50 |
| Cost of Hiring | £800.00 |
| Layoff Cost | £1,200.00 |
| Annual Cost | £38,800.00 |
Table 2: Annual Cost of new schedule
(Source: Ms-excel)
Table 2, chalked out a new production schedule for Acorn as certain new inclusions and deductions had been done within workforce. Therefore, this company has included certain employees for which hiring cost has increased with that expenditure has increased. Over here, the previously calculated annual schedule is considered as a decision variable within the utilized mathematical model (Ramin et al. 2018). Including this annual cost with the previous cost value, the company is not going to face any loss as layoff cost is going to provide support. Hiring cost is considered as an expenditure for the company whereas the layoff schedule is considered as an income.
Scenario C
- Based on the consideration of the scenario, it is noticeable that the Acorn will use a third party for the direct production. In this circumstance, Acorn will not be required to be involved in any manufacturing procedure as the third party will use up its own resources and will manufacture laptops (Zhen et al. 2020). As per the case study, per unit laptop production cost by the third party will be £26 including cost of component £20 per laptop.
The annual total cost of the schedule can be evaluated with the use of mathematical tools such as annual demand of laptop * cost per laptop. In this mathematical model, two variables are present such as cost per laptop = £26 and annual demand = 14,960. Hence, annual total cost will be 14,960*26 = 3,88,960.
- Report to manager
Acorn has decided to use third party for conducting organisational operations even after the selected third party is going to provide £26 per unit on manufacturing cost. This decision has been taken after following the underlined advantages and disadvantages within a third party contract. The company has to bear a minimal amount of extra manufacturing cost after considering £26 per unit which is definitely higher than average per unit lies within current market (Su et al. 2019). This company has decided to hire new employees and hiring those employees on a contractual basis is going to fulfil the requirements of overtime and inventory holding. According to the provided information, Acorn wants to increase over time from 20 hours to 40 hours and increase sales through demand for having inventory value zero at the end of a year. Observing this consideration the company will be beneficial in utilising third party concept for managing logistics operations in a cost-efficient manner.
Including third-party distribution and warehousing is going to help the company minimize the storage cost that lowers the annual production cost. Currently, this company is holding the storage cost which is equal to the inventory capacity. Therefore, holding a minimum amount of inventory is going to help in lowering operational costs. A decreasing operational cost determines the company is going to provide a higher sales commitment. Table 1, shows this commitment remains stable within the entire year. In order to increase optimal production, this company has to equalize marginal revenue with marginal cost. Bringing this equality a relationship will build up to where company is going to prepare future production cost of a product after observing the revenue earned from previous saleable products.
Inventory holding cost is an essential factor that is required in the business as the organization is required to store raw materials and finished goods. Based on the consideration of the situation of Acorn it has been found that the organization has a requirement of maintaining inventory cost of £3 per laptop. On the other hand, the cost of overtime is another considerable high direct expenditure to business (Meister et al. 2019). However, overtime costs provide a flexibility of working with a limited number of workers. In the scenario of Acorn, the company has a total of 3700 manpower and the company estimated that there might be 20 hours of overtime per month per laptop production. Considering these two expenditures, average cost of one laptop production increases.
Acorn after incorporating third party within operations per-unit cost will increase which is definitely higher than the average cost. In this scenario, the company’s internal management needs to increase the demand value then only output will increase that support in declining per-unit cost. Therefore, per-unit cost of a laptop is a decision criterion for this company where constraints are negative impacts of third-party (Basiri and Pourrahimian, 2018). In optimizing a production schedule the company is going to face a set of possible values in form of variables that are going to create an impact on decision criteria. As per the given scenario, this company has to face two different scenarios where a condition lies in including a third party and another condition where that third party is absent. In both of these scenarios “per-unit cost” is decision variable for this company for fulfilling requirements of operational demand.
Problem 3: Material Requirement Planning
a. Low-level-coded bill-of materials
“Materials Requirement Planning” (MRP) refers to a concept where an organisation uses computer-based planning related to production and controlling inventory systems. Most the organisation uses this technicality for making an attempt to achieve adequate inventory levels that match with required production level (Dimas Mukhlis et al. 2019). As per the given case study, the calculation of low-level coded bills has been done as for incorporating simplicity where various products are being produced with complex bills of materials.
| 1 | 2 | 3 | 4 | 5 | |
| Gross Requirements | 2 | 20 | 25 | 15 | |
| Scheduled Receipts | 5 | 30 | |||
| On-hand | 20 | 23 | 3 | 33 | 8 |
| Net Requirements | 7 | ||||
| Planned order Receipt | 7 | ||||
| Planned order release | 7 |
Table 3: Low-level coded bill of material
(Source: Ms-excel)
Computing the above low-level code of given scenario is going to register each item utilized within production operations to perform a level-by-level explosion. Observing the computed values it is easy to acquire information on each item manufactured or assembled within the production process of an end item (Hasanati et al. 2019). Production manager is going to get data related to part number, quantity per assembly, description, lead times, next higher assembly and finished item quantity.
b. Necessary planned order
According to the given information product A requires 80 and 150 units in Week 3 and 8 which makes a change in decision criteria for preparing the MRP schedule or coded bill. Plan related to material requirement for A has been developed and on that basis, product structure has been formulated including lead-time required for producing each component for respective weeks (Dimas Mukhlis et al. 2019). In order to determine gross requirements of component items in producing product A, depends on a plan chalked out for parent item. Release date related to planned order is being obtained through offsetting lead times.
References
Basiri, Z. and Pourrahimian, Y., 2018. Application of mathematical programming for stope layout and production schedule optimization in sublevel stoping. Mining Optim. Laboratory, 8(1), pp.235-245.
Braglia, M., Castellano, D., Frosolini, M. and Gallo, M., 2018. Overall material usage effectiveness (OME): a structured indicator to measure the effective material usage within manufacturing processes. Production Planning & Control, 29(2), pp.143-157.
Dimas Mukhlis, H.F., IndraEfrialdi, J. and Rimawan, E., 2019. Inventory Management using Demand Driven Material Requirement Planning for Analysis Food Industry. Int. J. Innov. Sci. Res. Technol, 4(7), pp.495-499.
Hasanati, N., Permatasari, E., Nurhasanah, N. and Hidayat, S., 2019, May. Implementation of material requirement planning (MRP) on raw material order planning system for garment industry. In IOP Conference Series: Materials Science and Engineering (Vol. 528, No. 1, p. 012064). IOP Publishing.
Liu, G., Chen, S., Jin, H. and Liu, S., 2021. Optimum opportunistic maintenance schedule incorporating delay time theory with imperfect maintenance. Reliability Engineering & System Safety, 213, p.107668.
Malik, A.I. and Kim, B.S., 2020. A multi-constrained supply chain model with optimal production rate in relation to quality of products under stochastic fuzzy demand. Computers & Industrial Engineering, 149, p.106814.
Meister, M., Beßle, J., Cviko, A., Böing, T. and Metternich, J., 2019. Manufacturing Analytics for problem-solving processes in production. Procedia CIRP, 81(5), pp.1-6.
Patnaik, S., Ashraf, M., Li, H., Knechtel, J. and Sinanoglu, O., 2021. Concerted wire lifting: Enabling secure and cost-effective split manufacturing. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 41(2), pp.266-280.
Ramin, D., Spinelli, S. and Brusaferri, A., 2018. Demand-side management via optimal production scheduling in power-intensive industries: The case of metal casting process. Applied Energy, 225, pp.622-636.
Su, J., Li, C., Zeng, Q., Yang, J. and Zhang, J., 2019. A green closed-loop supply chain coordination mechanism based on third-party recycling. Sustainability, 11(19), p.5335.
Tao, F., Fan, T., Jia, X. and Lai, K.K., 2021. Optimal production strategy for a manufacturing and remanufacturing system with return policy. Operational Research, 21(1), pp.251-271.
Xu, X.C., Gu, X.W., Wang, Q., Gao, X.W., Liu, J.P., Wang, Z.K. and Wang, X.H., 2018. Production scheduling optimization considering ecological costs for open production mines. Journal of cleaner production, 180, pp.210-221.
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